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A Parametric ROC Model-Based Approach for Evaluating the Predictiveness of Continuous Markers in Case–Control Studies

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  • Y. Huang
  • M. S. Pepe

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  • Y. Huang & M. S. Pepe, 2009. "A Parametric ROC Model-Based Approach for Evaluating the Predictiveness of Continuous Markers in Case–Control Studies," Biometrics, The International Biometric Society, vol. 65(4), pages 1133-1144, December.
    • Handle: RePEc:bla:biomet:v:65:y:2009:i:4:p:1133-1144
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2009.01201.x
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    References listed on IDEAS

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    1. Margaret Sullivan Pepe, 2000. "An Interpretation for the ROC Curve and Inference Using GLM Procedures," Biometrics, The International Biometric Society, vol. 56(2), pages 352-359, June.
    2. Kelly Zou & W. J. Hall, 2000. "Two transformation models for estimating an ROC curve derived from continuous data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 621-631.
    3. Ying Huang & Margaret Sullivan Pepe & Ziding Feng, 2007. "Evaluating the Predictiveness of a Continuous Marker," Biometrics, The International Biometric Society, vol. 63(4), pages 1181-1188, December.
    4. Margaret Sullivan Pepe & Tianxi Cai, 2004. "The Analysis of Placement Values for Evaluating Discriminatory Measures," Biometrics, The International Biometric Society, vol. 60(2), pages 528-535, June.
    5. Holly Janes & Margaret S. Pepe, 2008. "Matching in Studies of Classification Accuracy: Implications for Analysis, Efficiency, and Assessment of Incremental Value," Biometrics, The International Biometric Society, vol. 64(1), pages 1-9, March.
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    Cited by:

    1. Ma, Hua & Bandos, Andriy I. & Gur, David, 2018. "Informativeness of diagnostic marker values and the impact of data grouping," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 76-89.
    2. R. M. Pfeiffer & M. H. Gail, 2011. "Two Criteria for Evaluating Risk Prediction Models," Biometrics, The International Biometric Society, vol. 67(3), pages 1057-1065, September.

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